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March 2019, Volume 5, 1, pp 16-26

Network Traffic Analysis Using Queuing Model and Regression Technique

Samuel Adebayo Oluwadare


Oluwatoyin Catherine Agbonifo


Ayomikun Tinuola Babatunde

Samuel Adebayo Oluwadare 1 ,

Oluwatoyin Catherine Agbonifo 1 Ayomikun Tinuola Babatunde 1 
  1. Department of Computer Science, Federal University of Technology, Akure, Nigeria 1

Pages: 16-26

DOI: 10.18488/journal.104.2019.51.16.26

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Article History:

Received: 18 January, 2019
Revised: 25 February, 2019
Accepted: 28 March, 2019
Published: 09 May, 2019


The flow of network traffic on business and academic networks has been on the increase. This necessitates the issue of proper management of traffic network flow in order to ensure optimum performance. Network analysis looks at certain performance measures with a view to gaining insight into the pattern of flow in the network. This research employs a queuing model and regression technique to analyse the performance of the Federal University of Technology, Akure (FUTA) network. Traffic data flows were captured over a period of four weeks using Wireshark capturing tool at different strategic locations in the campus. The arrival rate and service rate were used to obtain the intensity of traffic at these locations. Analysis of the data assisted in determining the variability in the traffic flow. The major contribution of this research is that it developed an empirical model that identified variables that significantly determines network traffic. The model could assist network administrators to monitor, plan and improve on the quality of service.
Contribution/ Originality
This paper’s primary contribution is that it employed multiple regression to identify the factors that determine network traffic. The model could be used to plan the usage and monitoring of computer networks


Computer network analysis, Network traffic, Queuing model, Regression analysis.




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This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.


Prof. O.S. Adewale, Dean, School of Computing, The Federal University of Technology, Akure, Nigeria is hereby acknowledged for his encouragement in course of this research.

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